Safety Helmet Detection And License Plate Detection Using Advanced Yolov10

Abstract

This project presents an advanced computer vision system for realtime Safety Helmet Detection and License Plate Recognition using the latest YOLOv10 object detection architecture. The primary objective is to enhance workplace safety and vehicle monitoring by automatically identifying individuals without safety helmets in industrial zones and capturing vehicle license plates for surveillance and regulation purposes. YOLOv10, known for its superior accuracy and speed, enables efficient multi-object detection in complex environments. The system is trained on annotated datasets containing diverse helmet types and vehicle plates under varying conditions, ensuring robust performance. Wearing safety helmets can effectively reduce the risk of head injuries for construction workers in high-altitude falls. In order to address the low detection accuracy of existing safety helmet detection algorithms for small targets and complex environments in various scenes, this study proposes an improved safety helmet detection algorithm based on YOLOv10.